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Kexin Rong
I am an Assistant Professor in the School of Computer Science at Georgia Tech. My lab studies systems and algorithms to improve the computational and human efficiency of large-scale data analytics and is part of the Georgia Tech database group. I also spend time at VMware Research Group as an affiliated researcher.
I am broadly interested in building systems and tools to help democratize data science, i.e., making it easy for non-experts to make sense and leverage the increasing large volumes of data by making the process more efficient and more accessible.
Previously, I completed my Ph.D. in CS from Stanford (advised by Peter Bailis and Philip Levis) and my B.S. in CS from Caltech.
I am actively looking for master and PhD students. If you are a GT student who is interested in working with me, please check out this page.
Email /
Google Scholar /
Bio /
CV /
Lab Website
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Publications and Preprints
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Inshrinkerator: Compressing Deep Learning Training Checkpoints via Dynamic Quantization
Amey Agrawal, Sameer Reddy, Satwik Bhattamishra, Venkata Prabhakara Sarath Nookala, Vidushi Vashishth, Kexin Rong, Alexey Tumanov
To appear at SoCC 2024.
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SketchQL: Video Moment Querying with a Visual Query Interface
Renzhi Wu*, Pramod Chunduri*, Ali Payani, Xu Chu, Joy Arulraj, Kexin Rong
SIGMOD 2025.
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Lotus: Characterization of Machine Learning Preprocessing Pipelines via Framework and Hardware Profiling
Rajveer Bachkaniwala, Harshith Lanka, Kexin Rong, Ada Gavrilovska
IISWC 2024. (Best Paper Finalist)
[code]
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SketchQL Demonstration: Zero-shot Video Moment Querying with Sketches.
Renzhi Wu, Pramod Chunduri, Dristi Shah, Ashmitha Julius Aravind, Ali Payani, Xu Chu, Joy Arulraj, Kexin Rong.
VLDB 2024 Demo.
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Demonstration of VCR: A Tabular Data Slicing Approach to Understanding Object Detection Model Performance.
Jie Jeff Xu, Saahir Dhanani, Jorge Piazentin Ono, Wenbin He, Liu Ren, Kexin Rong
VLDB 2024 Demo.
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FALCON: Fair Active Learning using Multi-armed Bandits
Ki Hyun Tae, Hantian Zhang, Jaeyoung Park, Kexin Rong, Steven Euijong Whang
VLDB 2024.
[code]
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Dynamic Data Layout Optimization with Worst-case Guarantees
Kexin Rong, Paul Liu, Sarah Ashok Sonje, Moses Charikar
ICDE 2024.
[slides][code]
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Scaling a Declarative Cluster Manager Architecture with Query Optimization Techniques
Kexin Rong, Mihai Budiu, Athinagoras Skiadopoulos, Lalith Suresh, Amy Tai
VLDB 2023.
[slides] [code]
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DiffPrep: Differentiable Data Preprocessing Pipeline Search for Learning over Tabular Data
Peng Li, Zhiyi Chen, Xu Chu, Kexin Rong
SIGMOD 2023.
[slides] [code]
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Interactive Demonstration of EVA
Gaurav Tarlok Kakkar, Aryan Rajoria, Myna Prasanna Kalluraya, Ashmita Raju, Jiashen Cao, Kexin Rong, Joy Arulraj
VLDB 2023 Demo.
[code]
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Improving Computational and Human Efficiency in Large-Scale Data Analytics
Kexin Rong
PhD Thesis 2021. (SIGMOD Doctoral Dissertation Award Honorable Mention)
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Approximate Partition Selection for Big-Data Workloads using Summary Statistics
Kexin Rong, Yao Lu, Peter Bailis, Srikanth Kandula, Philip Levis
VLDB 2020.
[talk]
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Rehashing Kernel Evaluation in High Dimensions
Paris Siminelakis*, Kexin Rong*, Peter Bailis, Moses Charikar, Philip Levis.
ICML 2019. (Long talk)
[blog] [code] [supplementary]
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CrossTrainer: Practical Domain Adaptation with Loss Reweighting
Justin Chen, Edward Gan, Kexin Rong, Sahaana Suri, Peter Bailis.
SIGMOD DEEM Workshop 2019.
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Locality-Sensitive Hashing for Earthquake Detection: A Case Study of Scaling Data-Driven Science
Kexin Rong, Clara Yoon, Karianne Bergen, Hashem Elezabi, Peter Bailis, Philip Levis, Gregory Beroza.
VLDB 2018.
[blog] [video] [code] [seismology paper]
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MacroBase: Prioritizing Attention in Fast Data
Firas Abuzaid, Peter Bailis, Jialin Ding, Edward Gan, Samuel Madden, Deepak Narayanan, Kexin Rong, Sahaana Suri.
ACM TODS 2018. "Best of SIGMOD 2017" Special Issue.
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ASAP: Prioritizing Attention via Time Series Smoothing
Kexin Rong, Peter Bailis.
VLDB 2017.
[Datadog blog] [Timescale blog] [blog] [demo] [talk] [slides] [code]
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Prioritizing Attention in Fast Data: Principles and Promise
Peter Bailis, Edward Gan, Kexin Rong, Sahaana Suri.
CIDR 2017.
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MacroBase: Prioritizing Attention in Fast Data
Peter Bailis, Edward Gan, Samuel Madden, Deepak Narayanan, Kexin Rong, Sahaana Suri.
SIGMOD 2017 (Invited to ACM TODS "Best of SIGMOD 2017" Special Issue.)
[website] [code]
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Demonstration: MacroBase, A Fast Data Analysis Engine
Peter Bailis, Edward Gan, Kexin Rong, Sahaana Suri.
SIGMOD 2017 Demo.
- From Raw to Ready: Optimizing Data Curation for Machine Learning
VMware Research, July 2024, San Francisco Bay Area
[abstract][slides]
- Towards a Human-Centric Approach to Machine Learning Lifecycle Management
UCSD Database Seminar, May 2023, Virtual
[abstract]
- Learned Indexing and Sampling for Improving Query Performance in Big-Data Analytics
Stanford MLSys Seminar, April 2022, Virtual
[abstract][slides]
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